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了解随机信号自身的特性，包括均值（数学期望）、方差、相关函数、频谱及功率谱密度等。(Understanding of random signal their characteristics, including mean (mathematical expectation), variance, correlation function, spectrum and power spectral density.)

在MATLAB下实现FSK调制解调。并画出了每一步的波形以及功率谱密度图(in MATLAB under FSK modulation and demodulation. And the mapping out of every step of the waveform and power spectral density map)

获取计算机中的时间信息，创建界面仿真时钟，动态显示时间。(Get the time information in the computer, create interface simulation clock, dynamic display time.)

说明：  针对多通道卷积混叠模型,基于信息理论的最小熵准则,利用单时间点观测样本给出了一类多通道盲反卷积 方法,这种方法不同于其它方法的地方在于考虑了源和观测信号的上下文信息.(A blind deconvolution algorithmfor a multi- channel convolution mixing model was derived. The algorithm is based on a minimum entropy contrast of information theory. The difference of the algorithm with the others is that the context message is used in contrast.)

matlab遗传算法 matlab遗传算法matlab遗传算法matlab遗传算法matlab遗传算法(matlab genetic algorithm )

在Matlab环境下实现的无损编解码，代码简单易懂(In the lossless codec implementation under the environment of Matlab, the code is simple to understand)

信道估计程序，ieee发表的文章对应的原码，本人已经验证，可以使用。(Channel estimation procedures, ieee post corresponding to the original code, I have verified that you can use.)

贝叶斯分类算法是统计学的一种分类方法，它是一类利用概率统计知识进行分类的算法。在许多场合，朴素贝叶斯(Naï ve Bayes，NB)分类算法可以与决策树和神经网络分类算法相媲美，该算法能运用到大型数据库中，而且方法简单、分类准确率高、速度快。(Bayesian classification algorithm is a statistical classification method, which is a kind of knowledge to classify the use of probabilistic algorithms. In many cases, Naive Bayes (Naï ve Bayes, NB) classification algorithm with decision trees and neural network classification algorithm is comparable, the algorithm can be applied to large s, and the method is simple, accurate classification rate of speed.)

单边带调幅的Simulink仿真 单边带调幅的Simulink仿真(SSB single sideband amplitude modulation amplitude modulation of the Simulink simulation of the Simulink simulation)

In the computer science field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.[1] Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields.